Hand gesture classification with electromyography signals for robust hand prosthesis

Abstract

Classifying hand gestures with EMG signals enables users to control newlineand command their prostheses through natural and intuitive movements, newlinemirroring the intricate gestures of a natural hand. This allows individuals with newlinelimb loss to seamlessly perform a wide range of daily tasks from grasping newlineobjects to manipulating tools, promoting independence and improving their newlineoverall quality of life. Moreover, efficient hand gesture classification newlinecontributes to the development of more responsive and adaptive prosthetic newlinesystems, fostering a closer integration between humans and machines in the newlinecontext of assistive technologies. newlineAn accurate and robust EMG-based Pattern Recognition (PR) system is newlinecrucial for developing the prosthetic controller to operate a myoelectric newlineprosthetic hand. Classifying hand gestures with EMG signals enables users to newlinecontrol and command their prostheses through natural and intuitive newlinemovements, mirroring the intricate gestures of a natural hand. Real-time hand newlineprostheses face challenges in achieving precision and natural control of hand newlinemovements. This research contributes to the methodologies required for newlineenhancing precision, control, and adaptability in prosthetic hands, thereby newlineimproving the overall functionality and user experience in hand prosthetics. newline